Summary of A Survey on Neural Topic Models: Methods, Applications, and Challenges, by Xiaobao Wu et al.
A Survey on Neural Topic Models: Methods, Applications, and Challenges
by Xiaobao Wu, Thong Nguyen, Anh Tuan Luu
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive survey on neural topic models (NTMs), which have gained significant attention in recent years due to their scalability and flexibility. Unlike traditional topic models, NTMs directly optimize parameters without requiring model-specific derivations, making them well-suited for large-scale applications. The authors systematically organize current NTM methods according to their network structures and introduce approaches for various scenarios, including short texts and bilingual documents. Additionally, the paper discusses a wide range of popular applications built on NTMs and highlights the challenges confronted by these models to inspire future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neural topic models are new ways to understand what topics are hidden in big collections of text, like books or articles. They’re better at handling huge amounts of data than older methods, which makes them useful for things like recommending related content or analyzing big social media platforms. This paper looks at all the different approaches people have taken to use neural topic models and how they can be used in various situations, such as working with short texts or documents written in multiple languages. |
Keywords
* Artificial intelligence * Attention
